ModelingLogisticRegression_Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IBM HR Employee Attrition\n",
    "#### @ Author - Mohammed\n",
    "**** [Website](https://datascience52.wordpress.com/) | [Github](https://github.com/mmd52) | [LinkedIn](https://www.linkedin.com/in/mohammedtopiwalla) ****\n",
    "\n",
    "### Logistic Regression Classification\n",
    "\n",
    "| Model Name | Accuaracy | Kappa Score | ROC AUC Curve value |\n",
    "| ------------ | ---------------- | ---------------- | ---------------- | \n",
    "| Logistic Regression with normal data | 88.04% | 0.42 | 0.67 |\t\n",
    "| Logistic Regression with smote data | 78.53% | 0.35 | 0.73 |\n",
    "\n",
    "> Seems like the model with normal data performs best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== BLOCK 1 Started! ======================\n",
      "==================== Packages Loaded ======================\n"
     ]
    }
   ],
   "source": [
    "#Loading all Packages\n",
    "print(\"==================== BLOCK 1 Started! ======================\")\n",
    "import pandas as pd\n",
    "pd.options.display.max_rows=10\n",
    "pd.options.display.max_columns=50\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import numpy as np\n",
    "n=40\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import time\n",
    "import sklearn\n",
    "from sklearn import svm\n",
    "from sklearn.cross_validation import train_test_split, cross_val_score\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "%matplotlib inline\n",
    "r_state=52\n",
    "print(\"==================== Packages Loaded ======================\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================== Data Loading ======================\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1470 entries, 0 to 1469\n",
      "Data columns (total 51 columns):\n",
      "Age                                  1470 non-null int64\n",
      "Attrition                            1470 non-null int64\n",
      "BusinessTravel                       1470 non-null int64\n",
      "DailyRate                            1470 non-null int64\n",
      "DistanceFromHome                     1470 non-null int64\n",
      "Education                            1470 non-null int64\n",
      "EmployeeCount                        1470 non-null int64\n",
      "EmployeeNumber                       1470 non-null int64\n",
      "EnvironmentSatisfaction              1470 non-null int64\n",
      "HourlyRate                           1470 non-null int64\n",
      "JobInvolvement                       1470 non-null int64\n",
      "JobLevel                             1470 non-null int64\n",
      "JobSatisfaction                      1470 non-null int64\n",
      "MonthlyIncome                        1470 non-null int64\n",
      "MonthlyRate                          1470 non-null int64\n",
      "NumCompaniesWorked                   1470 non-null int64\n",
      "OverTime                             1470 non-null int64\n",
      "PercentSalaryHike                    1470 non-null int64\n",
      "PerformanceRating                    1470 non-null int64\n",
      "RelationshipSatisfaction             1470 non-null int64\n",
      "StockOptionLevel                     1470 non-null int64\n",
      "TotalWorkingYears                    1470 non-null int64\n",
      "TrainingTimesLastYear                1470 non-null int64\n",
      "WorkLifeBalance                      1470 non-null int64\n",
      "YearsAtCompany                       1470 non-null int64\n",
      "YearsInCurrentRole                   1470 non-null int64\n",
      "YearsSinceLastPromotion              1470 non-null int64\n",
      "YearsWithCurrManager                 1470 non-null int64\n",
      "Department_Human Resources           1470 non-null int64\n",
      "Department_Research & Development    1470 non-null int64\n",
      "Department_Sales                     1470 non-null int64\n",
      "EducationField_Human Resources       1470 non-null int64\n",
      "EducationField_Life Sciences         1470 non-null int64\n",
      "EducationField_Marketing             1470 non-null int64\n",
      "EducationField_Medical               1470 non-null int64\n",
      "EducationField_Other                 1470 non-null int64\n",
      "EducationField_Technical Degree      1470 non-null int64\n",
      "Gender_Female                        1470 non-null int64\n",
      "Gender_Male                          1470 non-null int64\n",
      "JobRole_Healthcare Representative    1470 non-null int64\n",
      "JobRole_Human Resources              1470 non-null int64\n",
      "JobRole_Laboratory Technician        1470 non-null int64\n",
      "JobRole_Manager                      1470 non-null int64\n",
      "JobRole_Manufacturing Director       1470 non-null int64\n",
      "JobRole_Research Director            1470 non-null int64\n",
      "JobRole_Research Scientist           1470 non-null int64\n",
      "JobRole_Sales Executive              1470 non-null int64\n",
      "JobRole_Sales Representative         1470 non-null int64\n",
      "MaritalStatus_Divorced               1470 non-null int64\n",
      "MaritalStatus_Married                1470 non-null int64\n",
      "MaritalStatus_Single                 1470 non-null int64\n",
      "dtypes: int64(51)\n",
      "memory usage: 585.8 KB\n",
      "==================== Data Loaded In ======================\n",
      "Duration: --- 0.0015360434850056966 Minutes ---\n"
     ]
    }
   ],
   "source": [
    "print(\"==================== Data Loading ======================\")\n",
    "start_time = time.time()\n",
    "df=pd.read_csv('MainEncodedData.csv')\n",
    "del df['Unnamed: 0']\n",
    "df.info()\n",
    "print(\"==================== Data Loaded In ======================\")\n",
    "print(\"Duration: --- %s Minutes ---\" % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>...</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>Department_Human Resources</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
       "      <th>EducationField_Human Resources</th>\n",
       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>EducationField_Marketing</th>\n",
       "      <th>EducationField_Medical</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>JobRole_Healthcare Representative</th>\n",
       "      <th>JobRole_Human Resources</th>\n",
       "      <th>JobRole_Laboratory Technician</th>\n",
       "      <th>JobRole_Manager</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1102</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5993</td>\n",
       "      <td>19479</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>279</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5130</td>\n",
       "      <td>24907</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1373</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2090</td>\n",
       "      <td>2396</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1392</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2909</td>\n",
       "      <td>23159</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>591</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3468</td>\n",
       "      <td>16632</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition  BusinessTravel  DailyRate  DistanceFromHome  Education  \\\n",
       "0   41          1               1       1102                 1          2   \n",
       "1   49          0               2        279                 8          1   \n",
       "2   37          1               1       1373                 2          2   \n",
       "3   33          0               2       1392                 3          4   \n",
       "4   27          0               1        591                 2          1   \n",
       "\n",
       "   EmployeeCount  EmployeeNumber  EnvironmentSatisfaction  HourlyRate  \\\n",
       "0              1               1                        2          94   \n",
       "1              1               2                        3          61   \n",
       "2              1               4                        4          92   \n",
       "3              1               5                        4          56   \n",
       "4              1               7                        1          40   \n",
       "\n",
       "   JobInvolvement  JobLevel  JobSatisfaction  MonthlyIncome  MonthlyRate  \\\n",
       "0               3         2                4           5993        19479   \n",
       "1               2         2                2           5130        24907   \n",
       "2               2         1                3           2090         2396   \n",
       "3               3         1                3           2909        23159   \n",
       "4               3         1                2           3468        16632   \n",
       "\n",
       "   NumCompaniesWorked  OverTime  PercentSalaryHike  PerformanceRating  \\\n",
       "0                   8         1                 11                  3   \n",
       "1                   1         0                 23                  4   \n",
       "2                   6         1                 15                  3   \n",
       "3                   1         1                 11                  3   \n",
       "4                   9         0                 12                  3   \n",
       "\n",
       "   RelationshipSatisfaction  StockOptionLevel  TotalWorkingYears  \\\n",
       "0                         1                 0                  8   \n",
       "1                         4                 1                 10   \n",
       "2                         2                 0                  7   \n",
       "3                         3                 0                  8   \n",
       "4                         4                 1                  6   \n",
       "\n",
       "   TrainingTimesLastYear  WorkLifeBalance  YearsAtCompany  \\\n",
       "0                      0                1               6   \n",
       "1                      3                3              10   \n",
       "2                      3                3               0   \n",
       "3                      3                3               8   \n",
       "4                      3                3               2   \n",
       "\n",
       "           ...           YearsSinceLastPromotion  YearsWithCurrManager  \\\n",
       "0          ...                                 0                     5   \n",
       "1          ...                                 1                     7   \n",
       "2          ...                                 0                     0   \n",
       "3          ...                                 3                     0   \n",
       "4          ...                                 2                     2   \n",
       "\n",
       "   Department_Human Resources  Department_Research & Development  \\\n",
       "0                           0                                  0   \n",
       "1                           0                                  1   \n",
       "2                           0                                  1   \n",
       "3                           0                                  1   \n",
       "4                           0                                  1   \n",
       "\n",
       "   Department_Sales  EducationField_Human Resources  \\\n",
       "0                 1                               0   \n",
       "1                 0                               0   \n",
       "2                 0                               0   \n",
       "3                 0                               0   \n",
       "4                 0                               0   \n",
       "\n",
       "   EducationField_Life Sciences  EducationField_Marketing  \\\n",
       "0                             1                         0   \n",
       "1                             1                         0   \n",
       "2                             0                         0   \n",
       "3                             1                         0   \n",
       "4                             0                         0   \n",
       "\n",
       "   EducationField_Medical  EducationField_Other  \\\n",
       "0                       0                     0   \n",
       "1                       0                     0   \n",
       "2                       0                     1   \n",
       "3                       0                     0   \n",
       "4                       1                     0   \n",
       "\n",
       "   EducationField_Technical Degree  Gender_Female  Gender_Male  \\\n",
       "0                                0              1            0   \n",
       "1                                0              0            1   \n",
       "2                                0              0            1   \n",
       "3                                0              1            0   \n",
       "4                                0              0            1   \n",
       "\n",
       "   JobRole_Healthcare Representative  JobRole_Human Resources  \\\n",
       "0                                  0                        0   \n",
       "1                                  0                        0   \n",
       "2                                  0                        0   \n",
       "3                                  0                        0   \n",
       "4                                  0                        0   \n",
       "\n",
       "   JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                              0                0   \n",
       "1                              0                0   \n",
       "2                              1                0   \n",
       "3                              0                0   \n",
       "4                              1                0   \n",
       "\n",
       "   JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        1   \n",
       "1                           1                        0   \n",
       "2                           0                        0   \n",
       "3                           1                        0   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             0                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  \n",
       "0                      0                     1  \n",
       "1                      1                     0  \n",
       "2                      0                     1  \n",
       "3                      1                     0  \n",
       "4                      1                     0  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1470, 51)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Main Data Set : 1470\n",
      "For Train Set : 826\n",
      "For Validation Set : 276\n",
      "For Test Set : 368\n"
     ]
    }
   ],
   "source": [
    "#start_time = time.time()\n",
    "\n",
    "train, test = sklearn.cross_validation.train_test_split(df, train_size = 0.75,random_state=r_state)\n",
    "train, val = sklearn.cross_validation.train_test_split(train, train_size = 0.75,random_state=r_state)\n",
    "print(\"For Main Data Set :\",df['Attrition'].count())\n",
    "print(\"For Train Set :\",train['Attrition'].count())\n",
    "print(\"For Validation Set :\",val['Attrition'].count())\n",
    "print(\"For Test Set :\",test['Attrition'].count())\n",
    "\n",
    "x_train=train.drop(train.columns[1], axis = 1)\n",
    "x_val=val.drop(val.columns[1], axis = 1)\n",
    "x_test=test.drop(test.columns[1], axis = 1)\n",
    "y_train=train['Attrition']\n",
    "y_val=val['Attrition']\n",
    "y_test=test['Attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc Train: 0.837772, Acc Val:0.829710\n",
      "Acc Train: 0.837772, Acc Val:0.829710\n",
      "Acc Train: 0.843826, Acc Val:0.833333\n",
      "Acc Train: 0.851090, Acc Val:0.833333\n",
      "Acc Train: 0.878935, Acc Val:0.836957\n",
      "Acc Train: 0.888620, Acc Val:0.840580\n",
      "Acc Train: 0.901937, Acc Val:0.840580\n",
      "Acc Train: 0.900726, Acc Val:0.840580\n",
      "Acc Train: 0.909201, Acc Val:0.847826\n",
      "Acc Train: 0.903148, Acc Val:0.840580\n",
      "Acc Train: 0.891041, Acc Val:0.840580\n",
      "Acc Train: 0.903148, Acc Val:0.844203\n",
      "Acc Train: 0.903148, Acc Val:0.829710\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAEyCAYAAADX3IgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNxZSqI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1597b296e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "C_values = [0.00075,0.001,0.005,0.01,0.05,0.1,0.5,1.0,5.0,10.0,50.0,100.0,500.0]\n",
    "prediction_error = []\n",
    "train_error = []\n",
    "\n",
    "for C_val in C_values:\n",
    "    clf = LogisticRegression(C=C_val,max_iter=10000,random_state=52)\n",
    "    clf.fit(x_train, y_train)\n",
    "    score_train = clf.score(x_train, y_train)\n",
    "    score_test = clf.score(x_val,y_val)\n",
    "    train_error.append(1-score_train)\n",
    "    prediction_error.append(1-score_test)\n",
    "    #val_error.append(1-score_val) \n",
    "    print(\"Acc Train: %f, Acc Val:%f\"%(score_train,score_test))\n",
    "plt.figure(1, figsize=(8, 5))\n",
    "plt.clf()\n",
    "plt.plot(range(len(C_values)),train_error,color='k',label='train')\n",
    "plt.plot(range(len(C_values)),prediction_error,color='r',label='test')\n",
    "plt.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=750, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=52, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression(C=10,max_iter=750,random_state=52)\n",
    "logreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation average accuracy: 0.884\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=52)\n",
    "modelCV = LogisticRegression()\n",
    "scoring = 'accuracy'\n",
    "results = model_selection.cross_val_score(modelCV, x_train, y_train, cv=kfold, scoring=scoring)\n",
    "print(\"10-fold cross validation average accuracy: %.3f\" % (results.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=52, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression(random_state=52,C=10)\n",
    "logreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True         0   1\n",
      "Predicted         \n",
      "0          303  35\n",
      "1            9  21\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.97      0.93       312\n",
      "          1       0.70      0.38      0.49        56\n",
      "\n",
      "avg / total       0.87      0.88      0.86       368\n",
      "\n",
      "0.880434782609\n",
      "Duration: --- 0.00014315048853556314 Minutes ---\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(random_state=52,C=10)\n",
    "logreg.fit(x_train, y_train)\n",
    "start_time = time.time()\n",
    "\n",
    "model=logreg\n",
    "pred=model.predict(x_test)\n",
    "cm_df = pd.DataFrame(confusion_matrix(y_test, pred).T, index=model.classes_,\n",
    "                     columns=model.classes_)\n",
    "cm_df.index.name = 'Predicted'\n",
    "cm_df.columns.name = 'True'\n",
    "print(cm_df)\n",
    "print(classification_report(y_test, pred))\n",
    "print(model.score(x_test,y_test))\n",
    "\n",
    "print(\"Duration: --- %s Minutes ---\" % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.67\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmaFXCjMzKyQC4WZmRX6H/Dk3tFf1knDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1597b5e4da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = roc_curve(y_test, pred)\n",
    " \n",
    "# Calculate the AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print('ROC AUC: %0.2f' % roc_auc)\n",
    " \n",
    "# Plot of a ROC curve for a specific class\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42760180995475106"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import cohen_kappa_score\n",
    "cohen_kappa_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now on smote Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since our data set is imbalanced lets try balancing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    692\n",
       "1    134\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "sm = SMOTE(random_state=r_state, ratio =1,k_neighbors=9,kind='svm')\n",
    "sdf=sm.fit_sample(x_train,y_train)\n",
    "x_train_smote=pd.DataFrame(list(sdf[0]),columns=x_train.columns)\n",
    "y_train_smote=pd.DataFrame(list(sdf[1]),columns=['Attrition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    692\n",
       "1    495\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_smote['Attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acc Train: 0.737152, Acc Val:0.717391\n",
      "Acc Train: 0.746420, Acc Val:0.721014\n",
      "Acc Train: 0.810447, Acc Val:0.742754\n",
      "Acc Train: 0.834035, Acc Val:0.760870\n",
      "Acc Train: 0.860152, Acc Val:0.775362\n",
      "Acc Train: 0.860152, Acc Val:0.771739\n",
      "Acc Train: 0.860152, Acc Val:0.760870\n",
      "Acc Train: 0.855939, Acc Val:0.768116\n",
      "Acc Train: 0.860152, Acc Val:0.760870\n",
      "Acc Train: 0.858467, Acc Val:0.764493\n",
      "Acc Train: 0.858467, Acc Val:0.764493\n",
      "Acc Train: 0.860994, Acc Val:0.768116\n",
      "Acc Train: 0.860152, Acc Val:0.764493\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAEyCAYAAADX3IgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEYkTCm0REZE4odAWERGJE/8ft494P+XVaFkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1597b61ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True         0   1\n",
      "Predicted         \n",
      "0          253  20\n",
      "1           59  36\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.81      0.86       312\n",
      "          1       0.38      0.64      0.48        56\n",
      "\n",
      "avg / total       0.84      0.79      0.81       368\n",
      "\n",
      "0.785326086957\n",
      "Duration: --- 0.0001639405886332194 Minutes ---\n",
      "ROC AUC: 0.73\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmZW1b92mXrLbxe0IAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1597b5e45c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kappa score is :0.35292022792\n"
     ]
    }
   ],
   "source": [
    "# Finding the best c value\n",
    "C_values = [0.00075,0.001,0.005,0.01,0.05,0.1,0.5,1.0,5.0,10.0,50.0,100.0,500.0]\n",
    "prediction_error = []\n",
    "train_error = []\n",
    "\n",
    "for C_val in C_values:\n",
    "    clf = LogisticRegression(C=C_val,max_iter=10000,random_state=52)\n",
    "    clf.fit(x_train_smote, y_train_smote)\n",
    "    score_train = clf.score(x_train_smote, y_train_smote)\n",
    "    score_test = clf.score(x_val,y_val)\n",
    "    train_error.append(1-score_train)\n",
    "    prediction_error.append(1-score_test)\n",
    "    #val_error.append(1-score_val) \n",
    "    print(\"Acc Train: %f, Acc Val:%f\"%(score_train,score_test))\n",
    "plt.figure(1, figsize=(8, 5))\n",
    "plt.clf()\n",
    "plt.plot(range(len(C_values)),train_error,color='k',label='train')\n",
    "plt.plot(range(len(C_values)),prediction_error,color='r',label='test')\n",
    "plt.legend(loc=1)\n",
    "plt.show()\n",
    "\n",
    "logreg = LogisticRegression(random_state=r_state,C=100)\n",
    "logreg.fit(x_train_smote, y_train_smote)\n",
    "start_time = time.time()\n",
    "\n",
    "model=logreg\n",
    "pred=model.predict(x_test)\n",
    "cm_df = pd.DataFrame(confusion_matrix(y_test, pred).T, index=model.classes_,\n",
    "                     columns=model.classes_)\n",
    "cm_df.index.name = 'Predicted'\n",
    "cm_df.columns.name = 'True'\n",
    "print(cm_df)\n",
    "print(classification_report(y_test, pred))\n",
    "print(model.score(x_test,y_test))\n",
    "\n",
    "print(\"Duration: --- %s Minutes ---\" % ((time.time() - start_time)/60))\n",
    "\n",
    "fpr, tpr, _ = roc_curve(y_test, pred)\n",
    " \n",
    "# Calculate the AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print('ROC AUC: %0.2f' % roc_auc)\n",
    " \n",
    "# Plot of a ROC curve for a specific class\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "print(\"Kappa score is :\"+str(cohen_kappa_score(y_test,pred)))\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}